Motion detection devices and systems

ABSTRACT

Motion detection devices and systems are described herein. One motion detection device includes an inertial measurement unit (IMU) configured to measure velocity, orientation, and gravitational forces of the motion detection device and a computing component. The computing component can be configured to determine spectrum parameters of a mobile vehicle associated with the motion detection device using measurements from the IMU, determine IMU orientation parameters using measurements from the IMU, and estimate motion of the mobile vehicle using the spectrum parameters, the IMU orientation parameters, measurements from the IMU, and a motion estimation function.

TECHNICAL FIELD

The present disclosure relates to motion detection devices and systems.

BACKGROUND

Mobile vehicles, such as a forklift or car, can be equipped withcomputers that include a screen. The screen can display information forvehicle status, productivity, and/or safety monitoring. When the mobilevehicle is moving, the screen may not display any information (e.g., goblank) to avoid distracting the driver's attention when driving. Whenthe mobile vehicle is stationary, the screen may display information assoon as possible to minimize productivity disruption.

It is therefore essential to detect a mobile vehicle's motion status inreal time. Traditionally, the motion can be detected by attachingsensors and cables to the mobile vehicle on the gas and brake pedals.However, in some instances, the attachment can be time consuming and mayvoid the warranty of the mobile vehicle. Further, if a mobile vehicle isleased, the attachment of sensors and cables may not be allowed by thelessor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B illustrates an example of a motion detection device inaccordance with one or more embodiments of the present disclosure.

FIG. 2 illustrates a flow diagram of an example of a process forestimating motion using a motion detection device in accordance with oneor more embodiments of the present disclosure.

FIG. 3A illustrates a flow diagram of an example of a process forperforming offline calibration using a motion detection device inaccordance with one or more embodiments of the present disclosure.

FIG. 3B illustrates a diagram of an example frequency graph inaccordance with one or more embodiments of the present disclosure.

FIG. 4 illustrates a flow diagram of an example of a process forperforming online calibration using a motion detection device inaccordance with one or more embodiments of the present disclosure.

FIG. 5 illustrates a flow diagram of an example of a process forperforming online motion estimation using a motion detection device inaccordance with one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Motion detection devices and systems are described herein. For example,one or more motion detection devices can include an inertial measurementunit (IMU) configured to measure velocity, orientation, andgravitational forces of the motion detection device and a computingcomponent. The computing component can be configured to determinespectrum parameters of a mobile vehicle associated with the motiondetection device using measurements from the IMU, determine IMUorientation parameters using measurements from the IMU, and estimatemotion of the mobile vehicle using the spectrum parameters, the IMUorientation parameters, measurements from the IMU, and a motionestimation function.

Mobile vehicles, such as a forklift or car, can be equipped withcomputers that include a screen. For instance, a computer on a forkliftcan be used to display information on where a driver should drive tonext (e.g., the next job and/or project), safety reminders and/ormonitoring, and/or can record productivity of the driver. To avoiddistracting the driver's attention when driving, it can be advantageousto not display any information on the screen (e.g., go blank) to avoiddistracting the driver's attention. When the mobile vehicle isstationary, the screen may display information as soon as possible tominimize productivity disruption. Thereby, the screen may be deactivated(e.g., go blank) when the vehicle is in motion and activated (e.g.,display information) when the vehicle is not in motion.

In order to properly activate and/or deactivate the screen, the mobilevehicle's motion status in real time can be detected. Traditionally, themotion can be detected by attaching sensors and cables to the mobilevehicle on the gas and brake pedals. However, in some instances, theattachment can be time consuming and may void the warranty of the mobilevehicle. Further, if a mobile vehicle is leased, the attachment ofsensors and cables may not be allowed by the lessor. For instance,forklifts are often leased, making it difficult to install such sensorsand cables.

By contrast, embodiments of the present disclosure include motiondetection devices and systems that can be externally attached to themobile vehicle and can estimate motion based on vibrations of the mobilevehicle. A motion detection device, in accordance with one or moreembodiments, can include an IMU that can measure velocity, orientation,and gravitational forces of the motion detection device using acombination of accelerometers, gyroscopes, and/or magnetometers.

Using the measurements, the motion detection device can estimate motionof a mobile vehicle that the motion detection device is attached to. Theestimation can include detection of motion and/or non-motion (e.g.,stationary) of the mobile vehicle based on a frequency of vibrationsmeasured. Using a motion detection device that can be externallyattached to a mobile vehicle can be easier to install, cheaper, and/ormay not void warranties as compared to past solutions, such as sensorsand cables.

These embodiments are described in sufficient detail to enable those ofordinary skill in the art to practice one or more embodiments of thisdisclosure. It is to be understood that other embodiments may beutilized and that process, electrical, and/or structural changes may bemade without departing from the scope of the present disclosure.

As will be appreciated, elements shown in the various embodiments hereincan be added, exchanged, combined, and/or eliminated so as to provide anumber of additional embodiments of the present disclosure. Theproportion and the relative scale of the elements provided in thefigures are intended to illustrate the embodiments of the presentdisclosure, and should not be taken in a limiting sense.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.Similar elements or components between different figures may beidentified by the use of similar digits.

As used herein, “a” or “a number of” refers to one or more. For example,“a number of parameters” can refer to one or more parameters.

FIGS. 1A-1B illustrates an example of a motion detection device 100 inaccordance with one or more embodiments of the present disclosure. Themotion detection device 100 can be attached (e.g., mounted) to a mobilevehicle and used to detect motion of the mobile vehicle based onfrequency of vibration measurements.

A mobile vehicle, as used herein, can include a machine that can moveand/or transport passengers or cargo using an energy source. Examplemobile vehicles can include a gas-powered car, an electric car, aforklift, a golf-cart, a motorcycle, among other gas and/or electricpowered vehicles.

As illustrated by FIG. 1A, a motion detection device 100 can include aninertial measurement unit (IMU) 102, a computing component 104, a userinterface 106, and a network interface 108. The network interface 108can allow for processing on another networked computing device or suchdevices can be used to obtain executable instructions for use withvarious embodiments provided herein.

A user-interface 106 can include hardware components and/orcomputer-readable instruction components for a user to interact with acomputing component of the motion detection device using audio commands,text commands, and/or images. A user, as used herein, can include adriver and/or other person associated with the mobile vehicle. Forinstance, the user-interface 106 can receive user inputs (e.g., asdiscussed further herein).

An IMU 102, as used herein, can include a component that can measureand/or track movement measurements (e.g., movement of the motiondetection device). As an example, the IMU component can measurevelocity, orientation, and/or gravitation forces of the location deviceusing a combination of accelerometers, gyroscopes, and/or pressuresensors. For instance, the IMU component can measure and/or monitoracceleration, rotation (e.g., pitch, roll, and yaw), and/or vibration ofthe motion detection device 100.

Although not shown in FIG. 1A for clarity and so not to obscureembodiments of the present disclosure, the computing component 104 caninclude a memory and a processor coupled to the memory. The memory canbe any type of storage medium that can be accessed by the processor toperform various examples of the present disclosure. For example, thememory can be a non-transitory computer readable medium having computerreadable instructions (e.g., computer program instructions) storedthereon that are executable by the processor to perform various examplesof the present disclosure.

The memory can be volatile or nonvolatile memory. The memory can also beremovable (e.g., portable) memory, or non-removable (e.g., internal)memory. For example, the memory can be random access memory (RAM) (e.g.,dynamic random access memory (DRAM) and/or phase change random accessmemory (PCRAM)), read-only memory (ROM) (e.g., electrically erasableprogrammable read-only memory (EEPROM) and/or compact-disc read-onlymemory (CD-ROM)), flash memory, a laser disc, a digital versatile disc(DVD) or other optical disk storage, and/or a magnetic medium such asmagnetic cassettes, tapes, or disks, among other types of memory.Further, the memory can be located in the computer-generated speechdevice, or internal to another computing component (e.g., enablingcomputer readable instructions to be downloaded over the Internet oranother wired or wireless connection).

In various embodiments of the present disclosure, the motion detectiondevice 100 can include one or more input devices. A user may entercommands and information into the motion detection device 100 throughthe input devices. Example input devices can include a keyboard, mouseand/or other point device, touch screen, microphone, joystick, game pad,scanner, wireless communication, etc. The input devices can be connectedto the motion detection device 100 through an interface, such as aparallel port, game port, or a universal serial bus (USB). A screen orother type of display device can also be connected to the system via auser interface 106, such as a video adapter. The screen can displaygraphical user information for the user.

The computing component 104, in various embodiments, can be used toperform a number of processes including an offline calibration, anonline calibration, and/or an online motion estimation process, asdiscussed further herein with regards to FIGS. 2-5. The computingcomponent 104, using measurements from the IMU 102 can, for instance,estimate motion of a mobile vehicle that the motion detection device 100is attached to (e.g., mounted to).

The user interface 106 can be configured to provide a display on ascreen. For instance, the user interface 106 can be configured toprovide a blank display on the screen in response to a determinationthat the mobile vehicle is in motion and/or provide a display on thescreen in response to a determination that the mobile vehicle is not inmotion (e.g., idle). A screen, as used herein, can be a device thatdisplays information. Example screens can include a liquid crystaldisplay (LCD), a cathode ray tube (CRT), a touch screen, a plasma,and/or an organic light-emitting diode (OLED), among other screens.

FIG. 1B illustrates a motion detection device 100 mounted on a mobilevehicle. The motion detection device 100 illustrated in FIG. 1B caninclude the same motion detection device 100 illustrated in FIG. 1A, forexample.

The motion detection device 100 can be used to estimate motion of themobile vehicle using IMU orientation parameters 112, 114, 116 and agravity vector 120. The IMU orientation parameters 112, 114, 116, asfurther discussed herein, can include IMU accelerometers readings alongX 116, Y 112, and Z 114 axes (e.g., directions). A gravity vector, asused herein, can include a direction and magnitude of gravitationalforces. The gravity vector 120 can be determined from a measurement ofthe IMU (e.g., IMU accelerometers readings along X 116, Y 112, and Z 114axes) and can be used to determine a vertical tilting angle of themotion detection device 100, as discussed further herein.

The IMU orientation parameters 112, 114, 116, and the gravity vector 120(and/or a maximized peak value of acceleration) can be used to determinea forward vector 118 of the mobile vehicle. The IMU orientationparameters 112, 114, 116, gravity vector 120, and forward vector 118 canbe determined in an online calibration process, as further discussed inconnection with FIGS. 2 and 4. A forward vector, as used herein, can bea horizontal straightforward moving direction and/or magnitude (e.g.,acceleration).

Determining a parameter and/or other features (such as IMU parameters,gravity vector, etc.), as used herein, intends to be calculating and/orotherwise identifying a numerical value of the parameter and/or otherfeature. Estimating a parameters and/or other features (such as theforward vector, motion, speed, etc.), as used herein, intends to becalculating and/or otherwise identifying a numerical value of theparameter and/or other feature that is an estimate (e.g., gives ageneral idea about the value, size, and/or cost).

FIG. 2 illustrates a flow diagram of an example of a process 230 forestimating motion using a motion detection device in accordance with oneor more embodiments of the present disclosure. The motion detectiondevice 100 illustrated in FIGS. 1A and 1B can be used to perform theprocess 230, for example. For example, the computing component 104illustrated in FIG. 1 can be used to perform the process 230.

At block 232, the mobile detection device can perform offlinecalibration. The offline calibration can include determining offlinecalibration parameters of a mobile vehicle associated with motiondetection device. Offline calibration, as used herein, can be a processperformed using the motion detection device attached (e.g., mounted) toa mobile vehicle that is not dependent on a particular driver (e.g., thedriver is not a factor). By contrast, an online calibration, asdiscussed further herein, can be a process performed using the motiondetection device attached to the mobile vehicle that is performed foreach particular driver (e.g., is dependent on the particular driver).

For instance, a number of offline calibration parameters can bedetermined using measurements from the IMU when the mobile vehicle isidle and/or in motion. Example offline calibration parameters caninclude spectrum parameters, time domain filter parameters, systemperformance characteristics, and/or window parameters, among otherparameters.

The mobile vehicle associated with the motion detection device, as usedherein, can be a mobile vehicle that the motion detection device isattached to (e.g., mounted on). The motion detection device can beattached to the mobile vehicle such that the Y-axis of the accelerometerof the IMU is aligned with a forward direction of the mobile vehicle.

Spectrum parameters, as used herein, can be parameters associated with aspectrum of frequency of vibrations of a mobile vehicle when the mobilevehicle is idle and/or in motion. An idle mobile vehicle can be astationary vehicle with an engine and/or other source of power running.A mobile vehicle in motion can be a moving mobile vehicle.

The spectrum parameters can include a cutoff frequency range of the idlemobile vehicle, among other parameters. A cutoff frequency range of theidle mobile vehicle can include a vibration frequency value of an idlemobile vehicle (e.g., a frequency associated with the vibration of anidle mobile vehicle). For example, the cutoff frequency range of theidle mobile vehicle can include a minimum frequency value thatcorresponds to an idle mobile vehicle. A frequency value below thecutoff frequency range of the idle mobile vehicle can include afrequency of a mobile vehicle in motion.

In various instances, the offline calibration parameters can include anumber of other parameters. For example, offline calibration parameterscan include exponentially weighted moving average (EWMA) parameters of amobile vehicle and/or window parameters, as discussed further inconnection with FIG. 3A.

Upon a change of IMU orientation (e.g., with a new driver), at block234, the mobile detection device can perform online calibration. Theonline calibration can be performed each time an IMU orientation ischanged (e.g., the positioning of the mobile detection device ischanges), such as a change in driver. The online calibration can includedetermining IMU orientation parameters using measurements from the IMUmeasured when the mobile vehicle is idle and/or in motion. The onlinecalibration can be used to determine the IMU orientation so that themobile vehicle vibration along a forward vector (e.g., the forwarddirection of the mobile vehicle) can be determined.

IMU orientation parameters can include IMU accelerometer readings on X,Y, and Z axes (e.g., directions). The IMU orientation parameters can bedetermined using the gravity vector. In some embodiments, the IMUorientation parameters can be determined using an acceleration peakmaximum, as discussed further in connection with FIG. 4, in addition tothe gravity vector.

At block 236, the mobile detection device can perform online motionestimation. Online motion estimation can include a real-time motiondetection of the mobile vehicle using measurements from the IMU of themotion detection device. Estimating motion, as used herein, can includedetecting whether the mobile vehicle is in motion using a motionestimation threshold. A motion estimation threshold, as used herein, isa cut-off frequency value used to classify whether a mobile vehicle isin motion or not (e.g., a frequency band selected in the offlinecalibration process). For example, the online motion estimation caninclude estimating motion of the mobile vehicle using the spectrumparameters, the IMU orientation parameters, measurements from the IMU,and a motion estimation function. The measurements from the IMU caninclude real-time measurements.

The motion estimation function, as used herein, can include a slidingwindow motion estimation function such as a sliding window DiscreteFourier Transformation function (DFT). A sliding window function, asused herein, can include a function that can be used to process datafrom a fixed length of time (e.g., a window). DFT, as used herein, caninclude a function that converts a finite list of equally spaced samplesof the function into a list of coefficients of a finite combination ofcomplex sinusoids, ordered by their frequencies, that has the samplevalues. A sliding window DFT, as used herein, can include a DFT appliedat each window of data.

FIG. 3A illustrates a flow diagram of an example of a process 332 forperforming offline calibration using a motion detection device inaccordance with one or more embodiments of the present disclosure. Theoffline calibration can be performed once for a mobile vehicle and/orperiodically (e.g., recalibration each week, each month, etc.).

The motion detection device 100 illustrated in FIGS. 1A and 1B can beused to perform the process 332, for example. For example, the computingcomponent 104 illustrated in FIG. 1 can be used to perform the process332.

At block 340, the mobile vehicle can be idle. An idle mobile vehicle canbe a mobile vehicle (e.g., an engine of the mobile vehicle) that ispowered and is not in motion. An idle mobile vehicle can, in variousinstances, create vibration of a particular frequency and/or frequencyrange. For example, the frequency and/or frequency range of an idlemobile vehicle can be a higher frequency than the frequency and/orfrequency range of a moving mobile vehicle.

At block 342, spectrum parameters can be determined using measurementsfrom the IMU measured when the mobile vehicle is idle and/or in motion.For instance, the frequency of the vibrations associated with an idlemobile vehicle can be compared to a frequency of vibrations associatedwith the mobile vehicle moving in a forward motion. The spectrumparameters can include a cutoff frequency range of the idle mobilevehicle (e.g., a minimum frequency peak of vibrations when the vehicleis idle) and/or a maximum frequency of the mobile vehicle when along aforward direction (e.g., a maximum frequency peak of vibrations when themobile vehicle is moving).

At block 344, time domain filter parameters of a mobile vehicleassociated with the motion detection device can be determined usingmeasurements from the IMU. Example time domain filter parameters caninclude exponentially weighted moving average (EWMA) parameters, aparameter for speed estimation, and/or a zero resetting parameter.

EWMA, as used herein, can include an infinite impulse response filterthat applies weighting factors which increase exponentially to calculatea moving average of data points of a dataset by creating a series ofaverages of different subsets of a full dataset. EWMA parameters canrefer to alpha the weighing factor for historical readings. A parameterfor speed estimation can be an estimated amplitude of velocity (e.g., ascalar number). A zero resetting parameter can include any systematicsensor reading drifting offsets for zero resetting in the online motiondetection process.

In various embodiments, as illustrated by FIG. 3A, the offlinecalibration can include, at block 346, moving the mobile vehicle in avariety of patterns. The movement in a variety of patterns can includemotion pattern benchmarking. Example movements can include forward,turning, backing up, lifting, etc.

At block 348, system performance can be characterized based on themotion patterns. System performance characteristics can includeparameters such as response delays (e.g., delays in measurements fromthe IMU), false positive rates (e.g., estimation of motion when idle),and/or false negative rates (e.g., estimation of idle when in motion),among other parameters.

At block 350, the process can include determining window parametersusing measurements from the IMU measured when mobile vehicle is movingand a sliding window motion estimation function. The window parameters,as used herein, can include window size, shift size, and/or motionestimation threshold, among other parameters for the sliding windowmotion estimation function.

The window parameters can be determined, for instance, by applying thesliding window motion estimation function to the measurements from theIMU when the mobile vehicle is moving under the variety of patterns(e.g., at block 346). For instance, the sliding window motion estimationfunction can be applied to the IMU measurements at each sliding window.

FIG. 3B illustrates a diagram of an example frequency graph 352 inaccordance with one or more embodiments of the present disclosure. Thefrequency graph 354 can include a signal spectrum of frequency ofvibration measured using a motion detection device associated with amobile vehicle. For example, the frequency graph 352 illustratesfrequency 356 of vibrations 354 of a mobile vehicle in motion 358 and/ora mobile vehicle that is not in motion (e.g., engine is idle) 360.

The frequency graph 354 can illustrate spectrum parameters. The spectrumparameters illustrated by the frequency graph 354 can include a cutofffrequency range of the idle mobile vehicle and/or a maximum frequency ofthe mobile vehicle when moving along a forward direction. For example, afrequency value of 18 HZ can include the cutoff frequency range of theidle mobile vehicle. That is, a measured frequency of vibration that isless than 18 HZ can be associated with a moving mobile vehicle and ameasured frequency of vibration that is greater than 18 HZ can beassociated with an idle mobile vehicle. A maximum frequency of themobile vehicle when moving along a forward direction can include 23 HZ.

FIG. 4 illustrates a flow diagram of an example of a process 434 forperforming online calibration using a motion detection device inaccordance with one or more embodiments of the present disclosure. Theonline calibration can be performed each time the motion detectiondevice orientation is changed.

The motion detection device 100 illustrated in FIGS. 1A and 1B can beused to perform the process 434, for example. For example, the computingcomponent 104 illustrated in FIG. 1 can be used to perform the process434.

For instance, the online calibration can be used to determine IMUorientation so that the mobile vehicle vibration measured along aforward vector can be estimated (e.g., during the online motionestimation as discussed in connection with FIG. 5). A forward vector caninclude a horizontal straightforward moving direction and/or magnitude(e.g., acceleration). The forward vector can be estimated using the IMUorientation parameters. The IMU orientation parameters can be determinedusing measurements from the IMU measured when the mobile vehicle ismoving, idle, and/or off, as discussed herein.

In various embodiments, determining IMU orientation parameters caninclude determining a gravity vector and, in some instances, anacceleration peak maximum. A gravity vector, as used herein, can includea direction of gravitational forces. The gravity vector can be used todetermine a vertical tilting angle of the motion detection device.

The acceleration peak maximum can include a maximum frequency value ofacceleration of the mobile vehicle. The acceleration peak maximum can beused to determine a horizontal tilting angle.

For example, at block 462, the process 434 can include locating themobile vehicle on a flat surface with the engine off. Measurements fromthe IMU while the engine is off, at block 464, can be used to determinethe gravity vector. The gravity vector can be determined, for instance,directly from accelerometer readings of the IMU along X, Y, and Z axes.

At block 466, the gravity vector can be used to determine a verticaltilting angle of the motion detection device. If the motion detectiondevice is mounted on a U-structure with only one tilting degree offreedom, the gravity vector can be used to estimate the forward vector,as discussed further herein.

Alternatively, if the motion detection device is mounted on a ball headallowing arbitrary orientation, at block 468, the mobile vehicle canmove straight forward on a flat surface. For instance, the movement caninclude the mobile vehicle moving straight forward from a stationarystate for a minimum amount of time.

At block 470, acceleration peak maximum can be determined usingmeasurements from the IMU measured when the mobile vehicle is moving(e.g., straight forward at block 468). An acceleration peak maximum, asused herein, can include a maximized acceleration peak of IMUmeasurements. Maximizing the acceleration peak can include adjusting X,Y, Z values to maximize the highest acceleration peak in the equation ofaX+bY+Cz, as discussed further herein. Using the acceleration peakmaximum, at block 472, the horizontal tilting angle of the motiondetection device can be determined.

At block 474, a forward vector can be determined using the IMUorientation parameters. For instance, the forward motion vector can berepresented by aX+bY+Cz, wherein the IMU orientation parameters are (X,Y, and Z). For example, (X, Y, and Z) can include accelerometer readingsfrom the IMU along X, Y, and Z axes (e.g., the gravity vector and/oracceleration peak maximum). And, (a, b, c) can represent forwarddirection.

FIG. 5 illustrates a flow diagram of an example of a process 536 forperforming online motion estimation using a motion detection device inaccordance with one or more embodiments of the present disclosure. Theonline motion estimation can include estimation of motion of the mobilevehicle using the spectrum parameters, the IMU orientation parameters,measurements from the IMU, and a motion estimation function.

The motion detection device 100 illustrated in FIGS. 1A and 1B can beused to perform the process 536, for example. For example, the computingcomponent 104 illustrated in FIG. 1 can be used to perform the process536.

At block 588, IMU measurements (e.g., real-time measurements) can befiltered. For example, the IMU measurements can be filtered using atleast one time domain filter parameter (e.g., EWMA parameter). Thefilter can include a gravitational correction and/or an offsetcorrection. The gravitational correction can include using the gravityvector to distill linear accelerations. The offset correction caninclude using at least one time domain filter parameter (e.g., zeroresetting parameter) to remove systematic sensor reading drifts.

At block 590, a forward vector can be estimated using the IMUorientation parameters (e.g., estimated the magnitude of the forwardvector using the forward direction determined at block 474 illustratedin connection with FIG. 4). In some embodiments, the data sampled maynot be at uniform intervals. In such embodiments, the data used for theforward vector can be resampled at a pre-defined frequency (such as 100HZ sampling rate).

In various embodiments, an optional time domain analysis can beperformed. For instance, if speed is to be estimated, at block 592, theresampled data can be filtered using a EWMA filter. The EWMA filter canuse at least one of the time domain filter parameter (e.g., a EWMAparameter) from the offline calibration process illustrated inconnection with FIG. 3A by block 344.

Further, at block 598, the speed of the mobile device can be estimated.The speed can be estimated based on one or more past speed estimates andcurrent IMU measurements. For instance, if it is known that the mobiledevice was moving at 10 miles per hour (mph) 1 second ago and no breakshave been applied, then it can be estimated that the mobile device isstill in motion. The speed estimated can be reasonably accurate when thepast motion detected was within 20-30 second or less and may becomeinaccurate over time.

At block 594, a sliding window motion estimation function can be appliedto filtered IMU parameters. For instance, the application of thefunction can be used to extract frequency measurements below a thresholdfrequency (e.g., low frequency measurements) from the IMU which are mostlikely induced by movement of the mobile vehicle. The thresholdfrequency can, for instance, be associated with a spectrum parameter(e.g., the cutoff frequency range of the idle mobile vehicle/the motionestimation threshold).

At block 596, motion of the mobile vehicle can be estimated using thefiltered IMU measurements, spectrum parameters, the IMU orientationparameters, window parameters, and the sliding window motion estimationfunction.

For instance, the application of the function at block 594 can be basedon the window parameters determined in the offline calibration processas illustrated in connection with FIG. 3A. Such parameters can includewindow size and shift size. Alternatively, a user can adjust the windowparameters.

Further, the cutoff frequency range determined in the offlinecalibration process, as illustrated in connection with FIG. 3A, can beapplied to the filtered IMU measurements to extract frequencymeasurements below a threshold frequency (e.g., low frequencymeasurements which are most likely induced by movement of the mobilevehicle).

Alternatively, a band-pass or band-stop filtering can be used to isolatemobile vehicle vibrations from vibrations induced by actual movement. Aband-pass filter can include a filter that passes frequencies within acertain range and rejects frequencies outside the range (e.g., theselected frequency band). A band-stop filter can include a filter thatpasses most frequencies unaltered but attenuates those in a specificrange (e.g., the selected frequency band).

And, the energy of vibrations below the threshold frequency (e.g., lowfrequency) can be estimated by selecting the maximum amplitude in theselected frequency band and/or integrate amplitude over the selectedfrequency band. The selected frequency band can include a frequency bandselected in the offline calibration process as described in connectionwith FIG. 3A (e.g., a motion estimation threshold).

The motion estimated can include an estimated speed and/or adetermination that the mobile device is in motion. For instance, a userinterface of the mobile device can be configured to provide a blankdisplay on a screen in response to a determination that the mobiledevice is in motion and/or provide a display on the screen in responseto a determination that the mobile device is not in motion (e.g., isidle).

That is, embodiments of the present disclosure include motion detectiondevices and systems that can be externally attached to the mobilevehicle. The motion detection device and/or system can detect motion inreal-time based on vibrations of the mobile vehicle using an IMU toestimate motion of mobile vehicle that the motion detection device isattached to. Using an externally attachment motion detection deviceand/or system that includes an IMU can be easier and/or cheaper toinstall, and/or may not void warranties as compared to past solutions.

Any of the above information, data, and/or images can be saved alongwith the plurality of images as metadata and/or a data file which can beavailable for later image processing and/or other purposes.

As used herein, “logic” is an alternative or additional processingresource to execute the actions and/or functions, etc., describedherein, which includes hardware (e.g., various forms of transistorlogic, application specific integrated circuits (ASICs), etc.), asopposed to computer executable instructions (e.g., software, firmware,etc.) stored in memory and executable by a processor.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anyarrangement calculated to achieve the same techniques can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments of thedisclosure.

It is to be understood that the above description has been made in anillustrative fashion, and not a restrictive one. Combination of theabove embodiments, and other embodiments not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description.

The scope of the various embodiments of the disclosure includes anyother applications in which the above structures and methods are used.Therefore, the scope of various embodiments of the disclosure should bedetermined with reference to the appended claims, along with the fullrange of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are groupedtogether in example embodiments illustrated in the figures for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the embodiments of thedisclosure require more features than are expressly recited in eachclaim.

Rather, as the following claims reflect, inventive subject matter liesin less than all features of a single disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

What is claimed:
 1. A motion detection device including: an inertialmeasurement unit (IMU) configured to measure velocity, orientation, andgravitational forces of the motion detection device; and a computingcomponent configured to: determine spectrum parameters of a mobilevehicle associated with the motion detection device using measurementsfrom the IMU, wherein spectrum parameters include a cutoff frequencyrange of the mobile vehicle when idle and a maximum frequency of themobile vehicle when moving along a forward direction; determine IMUorientation parameters using measurements from the IMU; estimate motionof the mobile vehicle using the spectrum parameters, the IMU orientationparameters, measurements from the IMU, and a motion estimation function;and deactivate information conveyed to a user in response to thespectrum parameters of the mobile vehicle being below the cutofffrequency range.
 2. The device of claim 1, wherein the motion estimationfunction includes a sliding window Discrete Fourier Transformationfunction (DFT).
 3. The device of claim 1, including a user interface toprovide a display on a screen in response to a determination that themobile vehicle is not in motion.
 4. The device of claim 1, wherein theIMU orientation parameters include IMU accelerometer readings along X,Y, and Z axes.
 5. The device of claim 1, wherein the computing componentconfigured to estimate motion includes the computing componentconfigured to detect whether the mobile vehicle is in motion using amotion estimation threshold.
 6. A motion detection device including: aninertial measurement unit (IMU) configured to measure velocity,orientation, and gravitational forces of the motion detection device;and a computing component configured to: determine spectrum parametersof a mobile vehicle associated with the motion detection device usingmeasurements from the IMU; determine IMU orientation parameters usingmeasurements from the IMU; estimate motion of the mobile vehicle usingthe spectrum parameters, the IMU orientation parameters, measurementsfrom the IMU, a motion estimation function, and a motion estimationthreshold; and deactivate information conveyed to a user in response tothe spectrum parameters of the mobile vehicle being below the cutofffrequency range.
 7. The device of claim 6, wherein the computingcomponent configured to determine the IMU orientation parametersincludes determining a gravity vector to determine a vertical tiltingangle.
 8. The device of claim 6, wherein the computing component isconfigured to filter the IMU measurements, wherein filtering the IMUmeasurements includes performing: gravity correction to distill linearaccelerations; and offset correction to remove systematic sensor readingdrifts using a zero resetting parameter.
 9. The device of claim 6,wherein the computing component is configured to perform anexponentially weighted moving average (EWMA) filter process in responseto resampling to 100 hertz.
 10. The device of claim 6, wherein thecomputing component is configured to estimate speed of the mobilevehicle based on a past speed estimate and current IMU measurements. 11.A non-transitory computer-readable medium storing instructionsexecutable by a processing resource to: an inertial measurement unit(IMU) configured to measure velocity, orientation, and gravitationalforces of the motion detection device; and a computing componentconfigured to: determine spectrum parameters of a mobile vehicleassociated with the motion detection device using measurements from theIMU, wherein spectrum parameters include a cutoff frequency range of themobile vehicle when idle and a maximum frequency of the mobile vehiclewhen moving along a forward direction; determine IMU orientationparameters using measurements from the IMU; estimate motion of themobile vehicle using the spectrum parameters, the IMU orientationparameters, measurements from the IMU, and a motion estimation function;and deactivate information conveyed to a user in response to thespectrum parameters of the mobile vehicle being below the cutofffrequency range.
 12. The medium of claim 11, wherein the instructions toestimate motion include instructions executable to estimate energy ofvibrations below a threshold frequency.
 13. The medium of claim 12,wherein the instructions to estimate an energy of vibrations below athreshold frequency include instructions executable to select a maximumamplitude in a selected frequency band.
 14. The medium of claim 12,wherein the instructions to estimate the energy of vibrations below thethreshold frequency include instructions executable to integrateamplitude over a selected frequency band.